AirLytics – Smart Air Quality Prediction System is a machine learning–based project designed to predict and analyze the Air Quality Index (AQI) using historical pollutant data along with temporal and weather parameters such as temperature, humidity, and wind speed.
The system applies data preprocessing, feature engineering, and a Random Forest regression model to generate accurate AQI predictions and analyze air pollution trends. AirLytics aims to support environmental monitoring, public awareness, and data-driven decision-making through reliable predictive analytics.
The objective of AirLytics – Smart Air Quality Prediction System is to develop an accurate and reliable machine learning–based solution for predicting the Air Quality Index (AQI) using historical pollutant data and weather parameters. The project aims to analyze air pollution patterns, support environmental monitoring, and enable data-driven decision-making for public health awareness and sustainable environmental management.
Phase
The AirLytics system has completed model training in Google Colab, with the backend deployed on Render and the frontend hosted on Netlify. The project is fully integrated and ready for demonstration, evaluation, and submission.
🚀 Project Launch
❓ Discovery
During the discovery phase, the team conducted extensive research on air quality monitoring systems, reviewed publicly available AQI datasets, and identified key pollutants (PM2.5, PM10, NO2, SO2, CO, O3) and meteorological factors affecting air quality predictions.
The primary goal was to understand the relationship between pollutant concentrations, temporal patterns, and weather conditions to build an accurate predictive model that could serve environmental monitoring needs.
💭 Design & Planning
The design and planning phase focused on architecting a three-tier system: a machine learning model trained in Google Colab, a Flask-based REST API backend deployed on Render, and a responsive React frontend hosted on Netlify.
The team designed the data pipeline to handle preprocessing, feature engineering, and model training using Random Forest regression, while planning the API endpoints for real-time AQI predictions and historical data visualization.
Key design decisions included selecting appropriate evaluation metrics (RMSE, MAE, R²), implementing CORS for cross-origin requests, and ensuring scalability through cloud deployment architecture.
🛠️ Development
The development phase involved implementing the complete machine learning pipeline, starting with data preprocessing and feature engineering in Google Colab using pandas and scikit-learn libraries.
A Random Forest regression model was trained on historical AQI data, achieving strong performance metrics (RMSE, MAE, R²) through hyperparameter tuning and cross-validation techniques.
The Flask-based REST API was developed with endpoints for AQI prediction and data retrieval, integrated with CORS support, and deployed on Render for cloud accessibility. Simultaneously, a responsive React frontend was built with interactive visualizations using Chart.js and deployed on Netlify, completing the full-stack integration.
🚛 Delivery
The AirLytics project has successfully completed all development phases and is now in the delivery stage. The machine learning model, backend API, and frontend interface are fully integrated and deployed across Google Colab, Render, and Netlify respectively. The system is currently undergoing final testing and documentation for project submission and demonstration.
🎓Status
The project is currently in the Delivery phase with all core components successfully deployed and operational. Final testing, documentation, and project demonstration preparations are underway for timely submission.
Summary
AirLytics successfully delivers a comprehensive air quality prediction system that combines machine learning accuracy with user-friendly visualization. The project demonstrates effective integration of data science, backend development, and frontend technologies to create a practical solution for environmental monitoring and public health awareness.
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<aside> 💡 Dataset Overview: AQI dataset
Describe the launch of the project; including timelines, success criteria, and general expectations.
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State,Date,PM2.5,PM10,NO,NO2,NOx,NH3,CO,SO2,O3,Benzene,Toluene,Xylene,AQI,AQI_Bucket,Year,Month,Season,Temperature,Humidity,Wind_Speed
Gujarat,2022-05-19,71.55000000000001,108.91,0.92,18.22,17.15,14.69,0.92,27.64,133.36,0.0,0.02,0.0,319.0,Very Poor,2022,5,Summer,20.0,46.2,12.1
Kerala,2022-06-01,71.55000000000001,108.91,0.97,15.69,16.46,14.69,0.97,24.55,34.06,3.68,5.5,3.77,319.0,Very Poor,2022,6,Monsoon,43.0,42.8,1.8
Meghalaya,2021-10-23,71.55000000000001,108.91,17.4,19.3,29.7,14.69,17.4,29.07,30.7,6.8,16.4,2.25,319.0,Very Poor,2021,10,Post-Monsoon,34.3,68.3,9.1
Jharkhand,2024-12-21,71.55000000000001,108.91,1.7,18.48,17.97,14.69,1.7,18.59,36.08,4.43,10.14,1.0,319.0,Very Poor,2024,12,Winter,28.9,52.4,13.0
Odisha,2021-06-12,71.55000000000001,108.91,22.1,21.42,37.76,14.69,22.1,39.33,39.31,7.01,18.89,2.78,319.0,Very Poor,2021,6,Monsoon,11.2,79.9,1.6
Karnataka,2024-05-15,71.55000000000001,108.91,45.41,38.48,81.5,14.69,45.41,45.76,46.51,5.42,10.83,1.93,319.0,Very Poor,2024,5,Summer,11.2,77.0,9.7
Meghalaya,2024-05-12,71.55000000000001,108.91,112.16,40.62,130.77,14.69,112.16,32.28,33.47,0.0,0.0,0.0,319.0,Very Poor,2024,5,Summer,7.3,43.4,7.9
Haryana,2024-10-26,71.55000000000001,108.91,80.87,36.74,96.75,14.69,80.87,38.54,31.89,0.0,0.0,0.0,319.0,Very Poor,2024,10,Post-Monsoon,39.6,72.7,5.3
Uttar Pradesh,2025-09-21,71.55000000000001,108.91,29.16,31.0,48.0,14.69,29.16,58.68,25.75,0.0,0.0,0.0,319.0,Very Poor,2025,9,Monsoon,29.0,57.1,2.0
Gujarat,2020-11-07,71.55000000000001,108.91,80.615,7.04,0.0,14.69,80.615,8.29,4.55,0.0,0.0,0.0,319.0,Very Poor,2020,11,Post-Monsoon,33.3,44.2,10.3
Bihar,2025-11-11,71.55000000000001,108.91,132.07,55.8,24.53,14.69,132.07,25.03,6.79,0.0,0.0,0.0,319.0,Very Poor,2025,11,Post-Monsoon,5.8,51.0,5.0
Punjab,2025-09-05,71.55000000000001,108.91,52.04,40.67,90.24,14.69,52.04,51.84,45.89,2.41,0.03,7.88,319.0,Very Poor,2025,9,Monsoon,43.8,87.2,5.4
Chandigarh,2023-10-11,71.55000000000001,108.91,48.82,44.2,87.09,14.69,48.82,68.21,35.16,9.45,13.35,12.5,319.0,Very Poor,2023,10,Post-Monsoon,38.3,47.0,10.9
Haryana,2025-05-03,71.55000000000001,108.91,19.2,27.86,33.05,14.69,19.2,52.65,20.96,2.16,2.26,5.19,319.0,Very Poor,2025,5,Summer,13.5,41.6,14.2
Bihar,2020-02-13,71.55000000000001,108.91,0.6,16.96,16.6,14.69,0.6,28.89,47.63,0.14,0.04,1.35,319.0,Very Poor,2020,2,Winter,12.3,46.4,5.3
Himachal Pradesh,2025-01-10,71.55000000000001,108.91,1.63,21.72,22.86,14.69,1.63,38.27,46.03,0.35,0.05,2.01,319.0,Very Poor,2025,1,Winter,12.3,36.1,11.3
Bihar,2022-11-07,71.55000000000001,108.91,11.44,24.73,34.75,14.69,11.44,49.5,52.24,0.68,0.0,3.27,319.0,Very Poor,2022,11,Post-Monsoon,17.2,60.8,14.4
Puducherry,2020-08-11,71.55000000000001,108.91,6.1,25.77,29.57,14.69,6.1,48.43,53.49,0.74,0.21,2.75,319.0,Very Poor,2020,8,Monsoon,26.0,31.1,1.9
Karnataka,2020-02-23,71.55000000000001,108.91,2.51,26.88,27.45,14.69,2.51,50.03,49.48,0.26,0.02,2.8,319.0,Very Poor,2020,2,Winter,22.3,81.3,8.2
Tamil Nadu,2023-01-28,71.55000000000001,108.91,7.92,26.8,32.4,14.69,7.92,58.87,56.37,0.24,0.01,3.97,319.0,Very Poor,2023,1,Winter,16.6,36.9,0.7
Telangana,2024-06-18,71.55000000000001,108.91,9.52,33.56,39.28,14.69,9.52,106.93,48.75,0.33,0.0,5.65,319.0,Very Poor,2024,6,Monsoon,29.5,64.9,12.5
West Bengal,2024-01-11,71.55000000000001,108.91,9.05,17.51,22.33,14.69,9.05,23.71,42.22,0.0,0.0,4.51,319.0,Very Poor,2024,1,Winter,10.6,29.4,4.7
Daman and Diu,2022-07-26,71.55000000000001,108.91,22.53,27.96,47.79,14.69,22.53,39.19,32.92,0.39,0.0,5.95,319.0,Very Poor,2022,7,Monsoon,16.7,93.4,5.3
Chhattisgarh,2025-03-05,71.55000000000001,108.91,2.03,20.39,21.4,14.69,2.03,40.07,32.49,0.47,0.7,1.54,319.0,Very Poor,2025,3,Summer,19.7,54.1,10.5
Kerala,2024-05-17,71.55000000000001,108.91,1.42,20.43,20.19,14.69,1.42,58.41,39.26,0.01,0.0,0.94,319.0,Very Poor,2024,5,Summer,23.2,81.7,11.9
West Bengal,2020-06-11,71.55000000000001,108.91,2.27,21.16,21.81,14.69,2.27,43.73,39.83,0.06,0.0,1.55,319.0,Very Poor,2020,6,Monsoon,36.4,77.6,6.3
Jharkhand,2024-11-22,71.55000000000001,108.91,2.19,21.7,23.26,14.69,2.19,43.28,41.1,0.02,0.0,1.65,319.0,Very Poor,2024,11,Post-Monsoon,13.0,73.5,10.2
Himachal Pradesh,2025-02-16,73.24,108.91,5.72,21.11,25.84,14.69,5.72,36.52,62.42,0.03,0.01,1.41,319.0,Very Poor,2025,2,Winter,25.6,82.1,14.4
Haryana,2023-12-04,83.13,108.91,6.93,28.71,33.72,14.69,6.93,49.52,59.76,0.02,0.0,3.14,209.0,Poor,2023,12,Winter,28.7,27.5,13.4
Madhya Pradesh,2022-08-16,79.84,108.91,13.85,28.68,41.08,14.69,13.85,48.49,97.07,0.04,0.0,4.81,328.0,Very Poor,2022,8,Monsoon,6.9,70.9,10.7
Total records (rows): State-wise daily observations
Total features (columns): Multiple environmental and temporal attributes
Missing values: Handled through preprocessing and imputation
Target variable (for ML): Air Quality Index (AQI)
This dataset represents air pollution and environmental monitoring data collected across different states. It is primarily used for regression-based AQI prediction and environmental trend analysis by combining pollutant concentrations with weather parameters.